Impact of islet autotransplantation on 30-day post-operative outcomes in patients undergoing total pancreatectomy: A NSQIP retrospective cohort analysis of 584 patients
Bibliographic record
Abstract
BACKGROUND: Total pancreatectomy (TP) offers a surgical option for refractory pancreatitis, yet confers substantial long-term morbidity associated with resultant diabetes. While total pancreatectomy with islet autotransplantation (TPIAT) offers an intuitive solution, data evaluating its safety have been limited to single-center studies. The aim of this study is to evaluate whether the addition of islet autotransplantation to TP confers additional post-operative morbidity within the 30-day post-operative period. METHODS: This is a retrospective cohort study of prospectively collected cases from the National Surgical Quality Improvement Program (NSQIP) database. Cases of TP with or without islet autotransplantation from 2016 to 2021 were included. Baseline demographics, and a comprehensive list of 30-day postoperative outcomes were evaluated. Multivariable logistic regression models were constructed to evaluate the impact of each factor on 30-day complications. RESULTS: A total of 584 cases were included with 171 (29.2 %) patients undergoing TPIAT. TPIAT patients were younger (58.8 vs. 39.5; p < 0.0001), and had lower incidences of pre-existing diabetes (41.4 % vs. 19.9 %; p < 0.0001) and hypertension (48.2 % vs. 24.6 %; p < 0.0001). TPIAT cohort had longer length of stay (10.3 days vs.12.2 days; p = 0.0006). There was no difference in overall rates of serious complications between the two cohorts (50.1 % vs. 45.0 %; p = 0.263). After adjusting for demographic differences between cohorts using multivariable logistic regression models, TPIAT was not associated with serious complications (OR 0.71; p = 0.168) compared to TP alone. CONCLUSION: The results from this study suggest that TPIAT does not appear to be associated with increased 30-day morbidity, and should be considered in patients to mitigate the long-term morbidity associated with diabetes mellitus post TP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".